Search results for "Algorithmic information theory"

showing 3 items of 3 documents

Shallow Reductionism and the Problem of Complexity in Psychology

2008

In his recent book The Mind Doesn't Work That Way, Fodor argues that computational modeling of global cognitive processes, such as abductive everyday reasoning, has not been successful. In this article the problem is analyzed in the framework of algorithmic information theory. It is argued that the failed approaches are characterized by shallow reductionism, which is rejected in favor of deep reductionism and nonreductionism.

Cognitive scienceReductionismAlgorithmic information theoryHistory and Philosophy of ScienceConnectionismPhilosophyCognitionGeneral PsychologyEpistemologyTheory & Psychology
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Algorithmic Information Theory and Computational Complexity

2013

We present examples where theorems on complexity of computation are proved using methods in algorithmic information theory. The first example is a non-effective construction of a language for which the size of any deterministic finite automaton exceeds the size of a probabilistic finite automaton with a bounded error exponentially. The second example refers to frequency computation. Frequency computation was introduced by Rose and McNaughton in early sixties and developed by Trakhtenbrot, Kinber, Degtev, Wechsung, Hinrichs and others. A transducer is a finite-state automaton with an input and an output. We consider the possibilities of probabilistic and frequency transducers and prove sever…

Discrete mathematicsAverage-case complexityAlgorithmic information theoryTheoryofComputation_COMPUTATIONBYABSTRACTDEVICESKolmogorov complexityDescriptive complexity theoryComputational physicsStructural complexity theoryTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESDeterministic finite automatonAsymptotic computational complexityComputer Science::Formal Languages and Automata TheoryComputational number theoryMathematics
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Combinatorial proofs of two theorems of Lutz and Stull

2021

Recently, Lutz and Stull used methods from algorithmic information theory to prove two new Marstrand-type projection theorems, concerning subsets of Euclidean space which are not assumed to be Borel, or even analytic. One of the theorems states that if $K \subset \mathbb{R}^{n}$ is any set with equal Hausdorff and packing dimensions, then $$ \dim_{\mathrm{H}} π_{e}(K) = \min\{\dim_{\mathrm{H}} K,1\} $$ for almost every $e \in S^{n - 1}$. Here $π_{e}$ stands for orthogonal projection to $\mathrm{span}(e)$. The primary purpose of this paper is to present proofs for Lutz and Stull's projection theorems which do not refer to information theoretic concepts. Instead, they will rely on combinatori…

FOS: Computer and information sciences28A80 (primary) 28A78 (secondary)General MathematicskombinatoriikkaCombinatorial proofComputational Complexity (cs.CC)01 natural sciencesCombinatoricsMathematics - Metric GeometryHausdorff and packing measures0103 physical sciencesClassical Analysis and ODEs (math.CA)FOS: Mathematics0101 mathematicsMathematicsAlgorithmic information theoryLemma (mathematics)Euclidean spacePigeonhole principle010102 general mathematicsOrthographic projectionHausdorff spaceMetric Geometry (math.MG)Projection (relational algebra)Computer Science - Computational ComplexityMathematics - Classical Analysis and ODEsfraktaalit010307 mathematical physicsmittateoria
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